Fatigue Crack Growth Prognostics by Particle Filtering and Ensemble Neural Networks

##plugins.themes.bootstrap3.article.main##

##plugins.themes.bootstrap3.article.sidebar##

Piero Baraldi Michele Compare Sergio Sauco Enrico Zio

Abstract

Particle Filtering (PF) is a model-driven approach widely used in prognostics, which requires models of both the degradation process and the measurement acquisition system. In many practical cases, analytical models are not available, but a dataset containing a number of pairs component state - corresponding measurement may be available.
In this work, a data-driven approach based on a bagged ensemble of Artificial Neural Networks (ANNs) is adopted to build an empirical measurement model of a Particle Filter for the prediction of the Residual Useful Life (RUL) of a structure whose degradation process is described by a stochastic fatigue crack growth model of literature. The work focuses on the investigation of the capability of the
proposed approach to cope with the uncertainty affecting the RUL prediction.

How to Cite

Baraldi, P., Compare, M., Sauco, S., & Zio, E. (2012). Fatigue Crack Growth Prognostics by Particle Filtering and Ensemble Neural Networks. PHM Society European Conference, 1(1). https://doi.org/10.36001/phme.2012.v1i1.1417
Abstract 92 | PDF Downloads 78

##plugins.themes.bootstrap3.article.details##

Keywords

particle filtering, RUL, Ensemble of ANNs

References
Arulampalam, M.S., Maskell, S., Gordon, N. and Clapp, T. (2002). A Tutorial on Particle Filters for Online Nonlinear/Non-Gaussian Bayesian Tracking, IEEE Transactions on Signal Processing, 50 (2), 174-188.
Baraldi, P., Di Maio, F. Zio, E., Sauco, S. Droguett, E., Magno, C. (2012) Ensemble of Neural Networks for Predicting Scale Deposition in Oil Well Plants Equipments, proceedings of PSAM 11 & ESREL 2012.
Breiman, L. (1999) Combining predictors, in Sharkey AJC (Ed.) Combining artificial neural nets: ensemble and modular multinet systems. Springer, Berlin Heidelberg New York, pp 31-50.
Cadini, F., Zio, E., Avram, D. (2009) Model-based Monte Carlo state estimation for condition-based component replacement, Reliability Engineering & System Safety, Vol. 94 (3), pp. 752-758.
Carney, J., Cunningham, P., & Bhagwan, U. (1999).
Confidence and prediction intervals for neural network ensembles. International Joint Conference on Neural Networks IJCNN, July 10-16, Washington D.C.
Coble, J.B. (2010) Merging Data Sources to Predict Remaining Useful Life – An Automated Method to Identify Prognostic Parameters. PhD diss., University of Tennessee.
Doucet, A., de Freitas, J.F.G. and Gordon, N.J. (2001) Sequential Monte Carlo methods in practice. Springer-Verlag, New York.
Gustaffson, F., & Saha, S. (2010). Particle filtering with dependent noise. In Proceedings of the 13th Conference on Information Fusion (FUSION). Edinburgh.
Heskes, T. (1997) Practical Confidence and Prediction Intervals, in M. Mozer, M. Jordan and T. Peskes, editors, Advances in Neural Information Processing Systems,vol 9, pages 466-472, Cambridge, 1997, MIT Press.
Hsu, C.W. , Chang, C.C., Lin, C.J. (2003) A Practical Guide to Support Vector Classification. Technical Report, 2003.
Liu, R., Ma, L., Kang, R. and Wang, N. (2011) The Modeling Method on Failure Prognostics Uncertainties in Maintenance Policy Decision Process, Proc. 9th Int. Conf. on Reliability, Maintainability and Safety (ICRMS 2011), pp. 815-820.
Moura, M.C., Lins, I.D., Ferreira, R.J., Droguett, E.L., Jacinto, C.M.C. Predictive maintenance policy for oil well equipment in case of scaling through support vector machines, in Proceedings of the European Safety and Reliability Conference-ESREL 2011, pp. 503-507.
Nix, D. and Weigend, A. (1994). Estimating the mean and the variance of the target probability distribution," in IEEE world congress on computational intelligence, International Joint Conference on Neural Networks, June 27-July 2, Orlando, Florida.Vol 1, pp. 55-60.
Orchard, M., Wu, B., Vachtsevanos, G. (2005) A Particle Filter Framework for Failure Prognosis, Proceedings of WTC2005 World Tribology Congress III. Washington D.C., USA, Sept. 12-16, 2005
Orchard, M. and Vachtsevanos, G. (2009) A Particle Filtering Approach for On-Line Fault Diagnosis and Failure Prognosis, Transactions of the Institute of Measurement and Control, Vol. 31 (3-4), pp. 221-246.
Papoulis, A. and Pillai, S.U. (2002) Probability, Random Variables and Stochastic Processes. McGraw-Hill Higher Education, 4th edition.
Saxena, A., Goebel K, Simon, D., Eklund, N. (2008) Damage Propagation Modeling for Aircraft Engine Run-to-Failure Simulation, in International Conference on Prognostics and Heath Management, PHM2008.
Stuart, G., Bienenstock, E. and Doursat, R. (1992) Neural Networks and the bias/variance dilemma. Neural Computation, Vol. 4(1), pp. 1-58.
Tang, L., Kacprzynski, G.J., Goebel, K. and Vachtsevanos, G. (2009) Methodologies for Uncertainty Management in Prognostics. Proc. IEEE Aerospace conference, 2009, pp. 1-12.
Vachtsevanos, G., Lewis, F.L., Roemer, M., Hess, A. and Wu, B. (2006) Intelligent Fault Diagnosis and Prognosis for Engineering Systems. John Wiley & Sons.
Zio. E. (2012) Prognostics and health management of industrial equipment, in Diagnostics and Prognostics of Engineering Systems: Methods and Techniques, S. Kadry, Eds. IGI-Global, 2012.
Section
Technical Papers